The goals are (1) to develop the mixture analysis method for application to functional imaging in cardiac PET studies, and eventually implemented in our functional imaging software packages; and (2) to implement a Bayesian estimator in the least squares optimization scheme to improve parameter estimation with a priori knowledge. The mixture analysis method represents voxel-level time-activity curve (TAC) as a weighted sum of sub-TACs associated with tissue types in the imaging volume. By mathematical modeling of the sub-TACs, physiological parameters, such as the regional myocardial flow and oxygen consumption, can be estimated to construct functional images. We will test mixture analysis on dynamic PET images of [O-15]water and [O-15]oxygen obtained from intact dog and human hearts. First, we will test the segmentation algorithm from methodology point of view as well as from physiological point of view, because mixture analysis provides a new means to quantify the physiological heterogeneity. Then, we will test the Bayesian estimator for parameter estimation using the sub-TACs. Finally, we will test the algorithm for constructing parametric images in the context of mixture analysis.